


import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors
@require_tokenizers
class 						lowerCamelCase_							(					lowerCAmelCase__  ,  unittest.TestCase					):



					'''simple docstring'''







					__UpperCAmelCase							  =					MvpTokenizer
					__UpperCAmelCase							  =					MvpTokenizerFast
					__UpperCAmelCase							  =					True
					__UpperCAmelCase							  =					filter_roberta_detectors






					def A   (					self    ) ->      List[Any]:







								'''simple docstring'''

								super().setUp()
								__lowercase			       = [
								    '''l''',
								    '''o''',
								    '''w''',
								    '''e''',
								    '''r''',
								    '''s''',
								    '''t''',
								    '''i''',
								    '''d''',
								    '''n''',
								    '''\u0120''',
								    '''\u0120l''',
								    '''\u0120n''',
								    '''\u0120lo''',
								    '''\u0120low''',
								    '''er''',
								    '''\u0120lowest''',
								    '''\u0120newer''',
								    '''\u0120wider''',
								    '''<unk>''',
								]
								__lowercase			       = dict(zip(snake_case_			,							range(len(snake_case_    )    )    )    )
								__lowercase			       = ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
								__lowercase			       = {'''unk_token''': '''<unk>'''}

								__lowercase			       = os.path.join(self.tmpdirname			,							VOCAB_FILES_NAMES['''vocab_file''']    )
								__lowercase			       = os.path.join(self.tmpdirname			,							VOCAB_FILES_NAMES['''merges_file''']    )
								with open(self.vocab_file			,							'''w'''			,							encoding='''utf-8'''    ) as fp:
											fp.write(json.dumps(snake_case_    ) + '''\n'''    )
								with open(self.merges_file			,							'''w'''			,							encoding='''utf-8'''    ) as fp:
											fp.write('''\n'''.join(snake_case_    )    )






					def A   (					self			,							**snake_case_    ) ->      Union[str, Any]:







								'''simple docstring'''

								kwargs.update(self.special_tokens_map    )
								return self.tokenizer_class.from_pretrained(self.tmpdirname			,							**snake_case_    )






					def A   (					self			,							**snake_case_    ) ->      Dict:







								'''simple docstring'''

								kwargs.update(self.special_tokens_map    )
								return self.rust_tokenizer_class.from_pretrained(self.tmpdirname			,							**snake_case_    )






					def A   (					self			,							snake_case_    ) ->      Optional[int]:







								'''simple docstring'''

								return "lower newer", "lower newer"






					@cached_property
					def A   (					self    ) ->      Union[str, Any]:







								'''simple docstring'''

								return MvpTokenizer.from_pretrained('''RUCAIBox/mvp'''    )






					@cached_property
					def A   (					self    ) ->      List[str]:







								'''simple docstring'''

								return MvpTokenizerFast.from_pretrained('''RUCAIBox/mvp'''    )






					@require_torch
					def A   (					self    ) ->      List[str]:







								'''simple docstring'''

								__lowercase			       = ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
								__lowercase			       = [0, 2_5_0, 2_5_1, 1_7_8_1_8, 1_3, 3_9_1_8_6, 1_9_3_8, 4, 2]

								for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											__lowercase			       = tokenizer(snake_case_			,							max_length=len(snake_case_    )			,							padding=snake_case_			,							return_tensors='''pt'''    )
											self.assertIsInstance(snake_case_			,							snake_case_    )

											self.assertEqual((2, 9)			,							batch.input_ids.shape    )
											self.assertEqual((2, 9)			,							batch.attention_mask.shape    )
											__lowercase			       = batch.input_ids.tolist()[0]
											self.assertListEqual(snake_case_			,							snake_case_    )
											# Test that special tokens are reset






					@require_torch
					def A   (					self    ) ->      int:







								'''simple docstring'''

								__lowercase			       = ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
								for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											__lowercase			       = tokenizer(snake_case_			,							padding=snake_case_			,							return_tensors='''pt'''    )
											# check if input_ids are returned and no labels
											self.assertIn('''input_ids'''			,							snake_case_    )
											self.assertIn('''attention_mask'''			,							snake_case_    )
											self.assertNotIn('''labels'''			,							snake_case_    )
											self.assertNotIn('''decoder_attention_mask'''			,							snake_case_    )






					@require_torch
					def A   (					self    ) ->      Union[str, Any]:







								'''simple docstring'''

								__lowercase			       = [
								    '''Summary of the text.''',
								    '''Another summary.''',
								]
								for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											__lowercase			       = tokenizer(text_target=snake_case_			,							max_length=3_2			,							padding='''max_length'''			,							return_tensors='''pt'''    )
											self.assertEqual(3_2			,							targets['''input_ids'''].shape[1]    )






					@require_torch
					def A   (					self    ) ->      int:







								'''simple docstring'''

								for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											__lowercase			       = tokenizer(
											    ['''I am a small frog''' * 1_0_2_4, '''I am a small frog''']			,							padding=snake_case_			,							truncation=snake_case_			,							return_tensors='''pt'''    )
											self.assertIsInstance(snake_case_			,							snake_case_    )
											self.assertEqual(batch.input_ids.shape			,							(2, 1_0_2_4)    )






					@require_torch
					def A   (					self    ) ->      Dict:







								'''simple docstring'''

								__lowercase			       = ['''A long paragraph for summarization.''']
								__lowercase			       = [
								    '''Summary of the text.''',
								]
								for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											__lowercase			       = tokenizer(snake_case_			,							text_target=snake_case_			,							return_tensors='''pt'''    )
											__lowercase			       = inputs['''input_ids''']
											__lowercase			       = inputs['''labels''']
											self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()    )
											self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()    )
											self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()    )
											self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()    )






					def A   (					self    ) ->      Dict:







								'''simple docstring'''

								pass






					def A   (					self    ) ->      Optional[int]:







								'''simple docstring'''

								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
											with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'    ):
														__lowercase			       = self.rust_tokenizer_class.from_pretrained(snake_case_			,							**snake_case_    )
														__lowercase			       = self.tokenizer_class.from_pretrained(snake_case_			,							**snake_case_    )
														__lowercase			       = '''A, <mask> AllenNLP sentence.'''
														__lowercase			       = tokenizer_r.encode_plus(snake_case_			,							add_special_tokens=snake_case_			,							return_token_type_ids=snake_case_    )
														__lowercase			       = tokenizer_p.encode_plus(snake_case_			,							add_special_tokens=snake_case_			,							return_token_type_ids=snake_case_    )

														# token_type_ids should put 0 everywhere
														self.assertEqual(sum(tokens_r['''token_type_ids''']    )			,							sum(tokens_p['''token_type_ids''']    )    )

														# attention_mask should put 1 everywhere, so sum over length should be 1
														self.assertEqual(
														    sum(tokens_r['''attention_mask''']    ) / len(tokens_r['''attention_mask''']    )			,							sum(tokens_p['''attention_mask''']    ) / len(tokens_p['''attention_mask''']    )			,							)

														__lowercase			       = tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']    )
														__lowercase			       = tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids''']    )

														# Rust correctly handles the space before the mask while python doesnt
														self.assertSequenceEqual(tokens_p['''input_ids''']			,							[0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]    )
														self.assertSequenceEqual(tokens_r['''input_ids''']			,							[0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]    )

														self.assertSequenceEqual(
														    snake_case_			,							['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']    )
														self.assertSequenceEqual(
														    snake_case_			,							['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']    )






from typing import Callable, Optional

from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream







class 							lowerCamelCase__						(				UpperCAmelCase   ):






			"""simple docstring"""






			def __init__(				self   ,    snake_case   ,    snake_case = None   ,    snake_case = None   ,    snake_case = False   ,    snake_case = False   ,    snake_case = None   ,    snake_case = None   ,    **snake_case   ,    ):

						'''simple docstring'''


						super().__init__(
						    features=snake_case   ,    cache_dir=snake_case   ,    keep_in_memory=snake_case   ,    streaming=snake_case   ,    num_proc=snake_case   ,    **snake_case   ,    )
						UpperCamelCase__       =							Generator(
						    cache_dir=snake_case   ,    features=snake_case   ,    generator=snake_case   ,    gen_kwargs=snake_case   ,    **snake_case   ,    )







			def 					snake_case__					(				self  ):

						'''simple docstring'''


						if self.streaming:
									UpperCamelCase__       =							self.builder.as_streaming_dataset(split="train"  )
						# Build regular (map-style) dataset
						else:
									UpperCamelCase__       =							None
									UpperCamelCase__       =							None
									UpperCamelCase__       =							None
									UpperCamelCase__       =							None

									self.builder.download_and_prepare(
									    download_config=snake_case   ,    download_mode=snake_case   ,    verification_mode=snake_case   ,    base_path=snake_case   ,    num_proc=self.num_proc   ,    )
									UpperCamelCase__       =							self.builder.as_dataset(
									    split="train"   ,    verification_mode=snake_case   ,    in_memory=self.keep_in_memory  )
						return dataset


